Data-driven algorithm for temperature predictions and corrections from low-resolution thermal images at fire scenes

Abstract

An accurate and efficient temperature measurement at fire scenes is crucial for structural safety predictions and fire emergency responses. The application of thermal images provides advantages of spatial and stable measurements over thermocouples. A data-driven algorithmic system for temperature measurement is proposed, utilizing thermal images and comprising a sequence of resolution enhancements, temperature predictions, and error corrections. The system starts with transformation of low-resolution images to super-resolution ones through convolutional neural networks (CNN) with hybrid scaling factors and attention fusion post-residual blocks. The temperatures are predicted from super-resolution thermal images based on cascade feedforward neural networks (CFNN) using a two-stage temperature division strategy. The errors of temperature predictions are corrected by comparing results between thermal images and thermocouples. The effectiveness, influencing factor and optimization strategy of the proposed system are validated through a series of large-scale fire tests. The mean absolute errors of temperature prediction models are within 20 °C, while over 70 % of error correction results are within ±30 °C. The proposed algorithm provides an effective tool to predict and correct temperature fields, aiming at a fast and smart fire emergency decision-making.